Bibliographic record
Abstract
There exist thousands of different minerals and other possible resources out in space. To exploit these resources and to further expand our knowledge of the universe, planetary exploration has opened new gates towards mankind. There are more than one hundred thousand designated asteroids located inside the asteroid belt. Some of these asteroids are as old as the Big Bang itself. Tracking of astronomical bodies such as asteroids is the new stream of research that has attracted a lot of attention. However, due to environmental constraints around asteroids, monolithic spacecraft missions seem challenging. Multi-agent systems, on the other hand, provide significant advantages when it comes to orbiting around asteroids. In this study, novel consensus algorithms are applied to regulate the multi-agent decentralized formation flying for increased system flexibility and reliability. A nonlinear controller is developed to control the decentralized formation flying system of interest. Faults are evaluated and reduced to a minimum when planning a mission. However, the performance of the controller should not be affected when faults occur. For this reason, sensor and actuator faults are examined in this thesis in conjunction with actuator limitations which is commonly referred to as saturation. The proposed control law is not only able to control the system while faults occur, but rather it is capable of maintaining system stability in the presence of time variant external disturbances. Uncertainty in parameters and dynamic models are inevitable due to the complexity of the relatively new mission and lack of experimental data about the system dynamics. As such, a novel adaptive robust control methodology is developed that does not require full knowledge of the system dynamics. Moreover, the adaptive robust control law is combined with a Chebyshev neural network to overcome system uncertainties. Numerical simulations results along with stability analyses show that the proposed control methodology is capable of reducing the system state error close to zero within 1 orbit when maximum thrust of 5 mN with bounded external disturbance of 3 mN is applied for formation reconfiguration scenarios; these results will be useful for the future formation flying missions around asteroids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".